IDEAS home Printed from https://ideas.repec.org/p/gtr/gatrjs/jfbr220.html
   My bibliography  Save this paper

Optimizing Cryptocurrency Portfolios: A Comparative Study of Rebalancing Strategies

Author

Listed:
  • Nichanan Sakolvieng

    (Martin de Tours School of Management and Economics, Assumption University, Thailand. Author-2-Name: Sutta Sornmayura Author-2-Workplace-Name: Martin de Tours School of Management and Economics, Assumption University, Thailand. Author-3-Name: Kaimook Numgaroonaroonroj Author-3-Workplace-Name: Martin de Tours School of Management and Economics, Assumption University, Thailand. Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:)

Abstract

" Objective - This study aims to contribute to the field of cryptocurrency portfolio management and rebalancing strategies by empirically investigating the impact of different allocation frequencies and threshold percentages on the risk-adjusted returns of cryptocurrency portfolios. Methodology/Technique – Utilizing a simulation of 10,000 cryptocurrency portfolios comprising seven assets, including Ethereum (ETH), Bitcoin (BTC), Tether (USDT), Litecoin (LTC), Solana (SOL), Dogecoin (DOGE), and Polygon (MATIC), this study examines and compares the effects of different allocation frequencies (daily, weekly, and monthly) in time-based rebalancing and various threshold percentages (5%, 10%, and 15%) in threshold-based strategies on the portfolios' risk-adjusted returns, using the Sharpe ratio. The performance of these strategies is also compared with a passive buy-and-hold strategy. Findings – The research reveals statistically significant differences in the risk-adjusted returns between the buy-and-hold strategy and the daily rebalancing and threshold-based strategies with 5% and 10% threshold percentages. The daily rebalancing strategy demonstrates a higher Sharpe ratio, while lower threshold percentages lead to better risk-adjusted returns. Novelty – These empirical findings, using a simulation of 10,000 cryptocurrency portfolios, provide valuable insights into optimizing cryptocurrency portfolio performance through rebalancing strategies. Additionally, they highlight the effectiveness of implementing rebalancing techniques in cryptocurrency portfolios, contributing to the understanding of rebalancing optimization in this domain. Type of Paper - Empirical"

Suggested Citation

  • Nichanan Sakolvieng, 2024. "Optimizing Cryptocurrency Portfolios: A Comparative Study of Rebalancing Strategies," GATR Journals jfbr220, Global Academy of Training and Research (GATR) Enterprise.
    • Handle: RePEc:gtr:gatrjs:jfbr220
    DOI: https://doi.org/10.35609/jfbr.2024.8.4(1)
    as

    Download full text from publisher

    File URL: http://gatrenterprise.com/GATRJournals/JFBR/pdf_files/JFBR-Vol-8(4)/1.Sutta%20Sornmayura.pdf
    Download Restriction: http://gatrenterprise.com/GATRJournals/online_submission.html
    File URL: https://libkey.io/https://doi.org/10.35609/jfbr.2024.8.4(1)?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Evans Rozario & Samuel Holt & James West & Shaun Ng, 2020. "A Decade of Evidence of Trend Following Investing in Cryptocurrencies," Papers 2009.12155, arXiv.org.
    2. Jing, Ruixue & Rocha, Luis E.C., 2023. "A network-based strategy of price correlations for optimal cryptocurrency portfolios," Finance Research Letters, Elsevier, vol. 58(PC).
    3. Masood Tadi & Irina Kortchmeski, 2021. "Evaluation of Dynamic Cointegration-Based Pairs Trading Strategy in the Cryptocurrency Market," Papers 2109.10662, arXiv.org.
    4. Hubert Dichtl & Wolfgang Drobetz & Martin Wambach, 2016. "Testing rebalancing strategies for stock-bond portfolios across different asset allocations," Applied Economics, Taylor & Francis Journals, vol. 48(9), pages 772-788, February.
    5. A. Can Inci & Rachel Lagasse, 2019. "Cryptocurrencies: applications and investment opportunities," Journal of Capital Markets Studies, Emerald Group Publishing Limited, vol. 3(2), pages 98-112, October.
    6. Masood Tadi & Irina Kortchemski, 2021. "Evaluation of dynamic cointegration-based pairs trading strategy in the cryptocurrency market," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 38(5), pages 1054-1075, July.
    7. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    8. Stjepan Beguv{s}i'c & Zvonko Kostanjv{c}ar, 2019. "Momentum and liquidity in cryptocurrencies," Papers 1904.00890, arXiv.org.
    9. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    10. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    11. Rida Ahroum & Othmane Touri & Fatima-Zahra Sabiq & Boujemâa Achchab, 2018. "Investment strategies with rebalancing: How could they serve Sukuk secondary market?," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 18(2), pages 91-100, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    2. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    3. Eero Pätäri & Timo Leivo, 2017. "A Closer Look At Value Premium: Literature Review And Synthesis," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 79-168, February.
    4. John H. Cochrane, 1999. "New facts in finance," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 23(Q III), pages 36-58.
    5. David Peón & Anxo Calvo, 2012. "Using Behavioral Economics to Analyze Credit Policies in the Banking Industry," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 145-160.
    6. Luo, Bing, 2019. "Effects of auditor-provided tax services on book-tax differences and on investors' mispricing of book-tax differences," Advances in accounting, Elsevier, vol. 47(C).
    7. Alexander S. Sangare, 2005. "Efficience des marchés : un siècle après Bachelier," Revue d'Économie Financière, Programme National Persée, vol. 81(4), pages 107-132.
    8. Cristi Spulbar & Ramona Birau & Lucian Florin Spulbar, 2021. "A Critical Survey on Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH) and Fractal Markets Hypothesis (FMH) Considering Their Implication on Stock Markets Behavior," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1161-1165, December.
    9. Taufiq Choudhry & Ranadeva Jayasekera, 2015. "Level of efficiency in the UK equity market: empirical study of the effects of the global financial crisis," Review of Quantitative Finance and Accounting, Springer, vol. 44(2), pages 213-242, February.
    10. Bart Frijns & Aaron Gilbert & Alireza Tourani-Rad, 2011. "Heterogeneity and sentiment in the stock market," International Journal of Behavioural Accounting and Finance, Inderscience Enterprises Ltd, vol. 2(2), pages 140-151.
    11. Meredith Beechey & David Gruen & James Vickery, 2000. "The Efficient Market Hypothesis: A Survey," RBA Research Discussion Papers rdp2000-01, Reserve Bank of Australia.
    12. JULES H. van BINSBERGEN & CHRISTIAN C. OPP, 2019. "Real Anomalies," Journal of Finance, American Finance Association, vol. 74(4), pages 1659-1706, August.
    13. Linnenluecke, Martina K. & Chen, Xiaoyan & Ling, Xin & Smith, Tom & Zhu, Yushu, 2017. "Research in finance: A review of influential publications and a research agenda," Pacific-Basin Finance Journal, Elsevier, vol. 43(C), pages 188-199.
    14. Abul Shamsuddin & Jae H. Kim, 2010. "Short‐Horizon Return Predictability in International Equity Markets," The Financial Review, Eastern Finance Association, vol. 45(2), pages 469-484, May.
    15. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    16. Mu-En Wu & Wei-Ho Chung, 2019. "Empirical Evaluations on Momentum Effects of Taiwan Index Futures via Stop-Loss and Stop-Profit Mechanisms," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 629-648, March.
    17. Gikas Hardouvelis & George Papanastasopoulos & Dimitrios D. Thomakos & Tao Wang, 2007. "Accruals, Net Stock Issues and Value-Glamour Anomalies: New Evidence on their Relation," Working Paper series 47_07, Rimini Centre for Economic Analysis.
    18. Diniz-Maganini, Natalia & Diniz, Eduardo H. & Rasheed, Abdul A., 2021. "Bitcoin’s price efficiency and safe haven properties during the COVID-19 pandemic: A comparison," Research in International Business and Finance, Elsevier, vol. 58(C).
    19. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, March.
    20. Urquhart, Andrew & Hudson, Robert, 2013. "Efficient or adaptive markets? Evidence from major stock markets using very long run historic data," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 130-142.

    More about this item

    Keywords

    Cryptocurrency; Mean-Variance Optimization; Portfolio Management; Rebalancing Strategies; Risk-Adjusted Returns;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G19 - Financial Economics - - General Financial Markets - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gtr:gatrjs:jfbr220. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Prof. Dr. Abd Rahim Mohamad (email available below). General contact details of provider: http://gatrenterprise.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.